Effective Guided Image Filtering for Contrast Enhancement

Although the guided image filtering (GIF) has an excellent edge-preserving property, it is prone to suffer from the halo artifacts near the edges. Weighted GIF and gradient-domain GIF try to address the problem by incorporating an edge-aware weighting into GIF. However, they are very sensitive to the regularization parameter and the halo artifacts will become serious as the regularization parameter increases. Moreover, noise in the background is often amplified because of the fixed amplification factor for the detail layer. In this letter, an effective GIF is proposed for better contrast enhancement. First, the average of local variances for all pixels is incorporated into the cost function of GIF for preserving the edges accurately in the base layer. Second, the amplification factor for the detail layer is calculated in a content-adaptive way for suppressing the noise while boosting the fine details. Experimental results show that the proposed filter is more robust to the regularization parameter and can produce visually pleasing output images. Compared to GIF and its related filters, halo artifacts and noise are reduced or attenuated by the proposed filter significantly.